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Methods
Results
Resume
Microbial	eukaryotes	in	the	Movile Cave	chemosynthetic	ecosystem
1 Ecologie Systématique Evolution,	Centre	National	de	la	Recherche Scientifique (CNRS),	Université Paris-Sud,	Orsay,	France	;	2 Group	for	underwater	and	speleological	exploration,	Emil	Racovita Institute	of	Speleology,	Bucharest,	Romania	;	* Corresponding	author:	puri.lopez@u-psud.fr
Prospects Literature	references John	St.	John,	https://github.com/jstjohn/SeqPrep
Martin,	M.	Cutadapt removes	adapter	sequences	from	high-throughput	sequencing	reads.	EMBnet.journal 17, 10–12	(2011).
Rognes,	T.,	Flouri,	T.,	Nichols,	B.,	Quince,	C.	&	Mahé,	F.	VSEARCH:	a	versatile	open	source	tool	for	metagenomics.	PeerJ Prepr. 4, e2409v1	(2016).
Mahé,	F.,	Rognes,	T.,	Quince,	C.,	De	Vargas,	C.	&	Dunthorn,	M.	Swarm	v2:	highly-scalable	and	high-resolution	amplicon	clustering.	PeerJ 3, e1420	(2015).
Fu,	L.,	Niu,	B.,	Zhu,	Z.,	Wu,	S.	&	Li,	W.	CD-HIT:	Accelerated	for	clustering	the	next-generation	sequencing	data.	Bioinformatics 28, 3150–3152	(2012).
Guillou,	L.	et	al. The	Protist Ribosomal	Reference	database	(PR2):	A	catalog	of	unicellular	eukaryote	Small	Sub-Unit	rRNA sequences	with	curated	
taxonomy.	Nucleic	Acids	Res. 41, D597-604	(2013).
Quast,	C.	et	al. The	SILVA	ribosomal	RNA	gene	database	project:	Improved	data	processing	and	web-based	tools.	Nucleic	Acids	Res. 41, D590–D596	(2013).
Forti,	P.,	Galdenzi,	S.	&	Sarbu,	S.	M.	The	hypogenic caves:	A	powerful	tool	for	the	study	of	seeps	and	their	environmental	effects.	in	Continental	Shelf	
Research 22, 2373–2386	(2002).
Ø Consolidation	of	the	metabarcoding method	and	analyses	of	the	results	(divergent	OTUs)
Ø Metatranscriptomic approach	(Implementation,	Phylogenomics and	Activities	analyses)
Ø Share	worldwide	our	results	by	publishing
Reboul	G.	1,	Hillebrand-Voiculescu A.M.	2,	Bertolino,	P.	1,	Moreira	D.	1,	López-García P. *,1
Metabarcoding approach
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
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primer
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primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
Forward	
primer
Reverse	
primer
18	S	V4-V5	regionsMID MID
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
Trimming	primers	and	MIDs	- Removing	bad	quality	bases	- Discarding	too	short	sequences (cutadapt)
Reverse	
primer
part	of	18	S	V4-V5	
regions
MID
part	of	18	S	V4-V5	
regions
MID Forward	
primer
Reverse	
primer
part	of	18	S	V4-V5	
regions
MID
part	of	18	S	V4-V5	
regions
MID Forward	
primer
Reverse	
primer
part	of	18	S	V4-V5	
regions
MID
part	of	18	S	V4-V5	
regions
MID Forward	
primer
Merging	paired-end	reads (flash)
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions
18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regionsx1 x1 x2 x1
Dereplicating the	good	sequences (vsearch)
18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions x1x1x1x1
Clustering	sequences	in	OTUs (Swarm,	cd-hit-est)
18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions x2x3x3x1
OTU1 OTU2 OTU3 OTU4
Taxonomic	assignation (BlastN against	PR2	and	Silva	databases)
Aggregating	good	sequences
18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions x2x3x3x1
OTU1 OTU2 OTU3 OTU4
taxa1 taxa2 unknown taxa3
Selecting	OTUs
18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions 18	S	V4-V5	regions x2x3x3x1
OTU1 OTU2 OTU3 OTU4
taxa1 taxa2 unknown taxa3
OTU2 OTU3 OTU4Abundance
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Relative	abundance	analyses	using	arbitrary	criteria
(here	single	read	OTUs	are	discarded)
Sample	1 Sample	2 Sample	3
Alveolata Opisthokonta Stramenopiles
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
tot_reads
rank1
Alveolata Amoebozoa Archaeplastida Excavata Opisthokonta Rhizaria Stramenopiles
Ciliophora										
Choanoflagellida				
Fungi															
Ochrophyta										Stramenopiles_X					
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
tot_reads
rank2
Ciliophora										 Conosa														 Chlorophyta									 Streptophyta								 Metamonada										 Choanoflagellida				
Fungi															 Mesomycetozoa							 Cercozoa												 Ochrophyta										 Stramenopiles_X					
Alveolata
Archaeplastida
Hacrobia
Opisthokonta Rhizaria Stramenopiles
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
tot_reads
rank1
Alveolata Amoebozoa Apusozoa Archaeplastida Excavata Hacrobia Opisthokonta Rhizaria Stramenopiles
Apicomplexa									
Ciliophora										
Dinophyta											
Perkinsea											
Chlorophyta									
Fungi															
Cercozoa												
Radiolaria										
Ochrophyta										
Stramenopiles_X					
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
tot_reads
rank2
Apicomplexa									 Ciliophora										 Dinophyta											 Perkinsea											 Breviatea											
Apusomonadidae						 Chlorophyta									 Streptophyta								 Metamonada										 Haptophyta										
Katablepharidophyta	 Telonemia											 Choanoflagellida				 Fungi															 Metazoa													
Cercozoa												 Radiolaria										 Ochrophyta										 Stramenopiles_X					
CDhit OTUs
8,488 142,371
OTUs	>	1	read
301 134,184
multiple	samples
37 113,883
bh_cov >	70
bh_ident_score>80
34 106,386
bh_cov >	80
bh_ident_score>80
33 105,852
bh_cov >	90
bh_ident_score>80
30 88,418
OTUs	in	only	one	
sample
264 20,301
bh_cov >	80
bh_ident_score>80
238 18,885
bh_cov >	80
bh_ident_score>90
215 17,849
bh_cov >	90
bh_ident_score>80
207 4,793
bh_cov >	90
bh_ident_score>90
197 4,769
Acknowledgements Ø COST	Action	TD1308	“ORIGINS”	and	ERC	“ProtistWorld”	for	funding
Ø DEEM	team	for	the	nice	working	atmosphere
Results	using	OTUs	identified	in	at	least	one	sample Results	using	OTUs	identified	in	only	one	sample
8,488 142,371
nb OTUs nb reads
Legend
bh_cov:	blast	hit	coverage
bh_ident_score:	blast	hit	identity	score	
Multiplexed	Illumina	Mi-Seq
paired-end	sequencing
Conclusions
A	small	number	of	OTUs	(34)	but	a	high	number	of	reads	(106,386)	are	shared	by	the	3	samples.
This	means	that	there	is	a	low	microbial	eukaryote	diversity	in	the	Movile Cave.
Also,	Alveolata and	Stramenopiles seem	to	be	the	most	abundant	protist superphyla in	the	Movile Cave.
Movile cave	organization	(Forti et	al.	2002)
Movile cave	in	the	world	(google	maps)
Microbial	eukaryotes
Metazoa
Procaryotes
HAS	BEEN	
STUDIED
HAS	BEEN	
STUDIED
OUR	PROJECT
Protist diversityMetabarcoding 18S	analyses
Phylogenomic analysesMetatranscriptomic analyses
Sequencing	and	Bioinformatic approaches,	methods,	pipelines	and	analyses
A	high	number	of	OTUs	(197)	are	found	in	only	one	sample	but	a	small	number	of	reads	(4,769).
This	means	that	there	are	minor	divergent	species	in	the	Movile Cave	depending	on	the	environment	(plankton,	floating	mats).
Rhizaria (mostly	Cercozoa)	is	the	most	abundant	protist superphylum in	the	Movile Cave	according	to	our	results.
Also,	noted	the	presence	of	Apusozoa and	Hacrobia superphyla not	present	in	the	multiple	sample	analyses	(to	investigate).
Multiple	parameter	combinations	were	tested	in	order	
to	choose	the	best	couple	of	criteria	for	our	analyses
Sample	1 Sample	2 Sample	3
Suboxic biofilm
• floating	mat
• fixed	in	EtOH
Suboxic biofilm
• live	mat
• 2	months	at	room	
T° in	the	dark
Plankton
• all	water	filtered
• 0.2-200	µm	
fraction	retained
DNA	Extraction,	sample	preparation	for	sequencing
Legend
:	tag	to	remember	from	which
sample	comes	from	the	read
:	tag	to	select	or	discard	an	OTU
for	the	downstream	analyses
Movile cave	floating	mat
Microbial	eukaryotes	(Protists)

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Microbial eukaryotes in the Movile Cave chemosynthetic ecosystem

  • 1. Methods Results Resume Microbial eukaryotes in the Movile Cave chemosynthetic ecosystem 1 Ecologie Systématique Evolution, Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Orsay, France ; 2 Group for underwater and speleological exploration, Emil Racovita Institute of Speleology, Bucharest, Romania ; * Corresponding author: puri.lopez@u-psud.fr Prospects Literature references John St. John, https://github.com/jstjohn/SeqPrep Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011). Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ Prepr. 4, e2409v1 (2016). Mahé, F., Rognes, T., Quince, C., De Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3, e1420 (2015). Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012). Guillou, L. et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597-604 (2013). Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013). Forti, P., Galdenzi, S. & Sarbu, S. M. The hypogenic caves: A powerful tool for the study of seeps and their environmental effects. in Continental Shelf Research 22, 2373–2386 (2002). Ø Consolidation of the metabarcoding method and analyses of the results (divergent OTUs) Ø Metatranscriptomic approach (Implementation, Phylogenomics and Activities analyses) Ø Share worldwide our results by publishing Reboul G. 1, Hillebrand-Voiculescu A.M. 2, Bertolino, P. 1, Moreira D. 1, López-García P. *,1 Metabarcoding approach Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID Forward primer Reverse primer 18 S V4-V5 regionsMID MID 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions Trimming primers and MIDs - Removing bad quality bases - Discarding too short sequences (cutadapt) Reverse primer part of 18 S V4-V5 regions MID part of 18 S V4-V5 regions MID Forward primer Reverse primer part of 18 S V4-V5 regions MID part of 18 S V4-V5 regions MID Forward primer Reverse primer part of 18 S V4-V5 regions MID part of 18 S V4-V5 regions MID Forward primer Merging paired-end reads (flash) 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regionsx1 x1 x2 x1 Dereplicating the good sequences (vsearch) 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions x1x1x1x1 Clustering sequences in OTUs (Swarm, cd-hit-est) 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions x2x3x3x1 OTU1 OTU2 OTU3 OTU4 Taxonomic assignation (BlastN against PR2 and Silva databases) Aggregating good sequences 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions x2x3x3x1 OTU1 OTU2 OTU3 OTU4 taxa1 taxa2 unknown taxa3 Selecting OTUs 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions 18 S V4-V5 regions x2x3x3x1 OTU1 OTU2 OTU3 OTU4 taxa1 taxa2 unknown taxa3 OTU2 OTU3 OTU4Abundance 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Relative abundance analyses using arbitrary criteria (here single read OTUs are discarded) Sample 1 Sample 2 Sample 3 Alveolata Opisthokonta Stramenopiles 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% tot_reads rank1 Alveolata Amoebozoa Archaeplastida Excavata Opisthokonta Rhizaria Stramenopiles Ciliophora Choanoflagellida Fungi Ochrophyta Stramenopiles_X 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% tot_reads rank2 Ciliophora Conosa Chlorophyta Streptophyta Metamonada Choanoflagellida Fungi Mesomycetozoa Cercozoa Ochrophyta Stramenopiles_X Alveolata Archaeplastida Hacrobia Opisthokonta Rhizaria Stramenopiles 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% tot_reads rank1 Alveolata Amoebozoa Apusozoa Archaeplastida Excavata Hacrobia Opisthokonta Rhizaria Stramenopiles Apicomplexa Ciliophora Dinophyta Perkinsea Chlorophyta Fungi Cercozoa Radiolaria Ochrophyta Stramenopiles_X 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% tot_reads rank2 Apicomplexa Ciliophora Dinophyta Perkinsea Breviatea Apusomonadidae Chlorophyta Streptophyta Metamonada Haptophyta Katablepharidophyta Telonemia Choanoflagellida Fungi Metazoa Cercozoa Radiolaria Ochrophyta Stramenopiles_X CDhit OTUs 8,488 142,371 OTUs > 1 read 301 134,184 multiple samples 37 113,883 bh_cov > 70 bh_ident_score>80 34 106,386 bh_cov > 80 bh_ident_score>80 33 105,852 bh_cov > 90 bh_ident_score>80 30 88,418 OTUs in only one sample 264 20,301 bh_cov > 80 bh_ident_score>80 238 18,885 bh_cov > 80 bh_ident_score>90 215 17,849 bh_cov > 90 bh_ident_score>80 207 4,793 bh_cov > 90 bh_ident_score>90 197 4,769 Acknowledgements Ø COST Action TD1308 “ORIGINS” and ERC “ProtistWorld” for funding Ø DEEM team for the nice working atmosphere Results using OTUs identified in at least one sample Results using OTUs identified in only one sample 8,488 142,371 nb OTUs nb reads Legend bh_cov: blast hit coverage bh_ident_score: blast hit identity score Multiplexed Illumina Mi-Seq paired-end sequencing Conclusions A small number of OTUs (34) but a high number of reads (106,386) are shared by the 3 samples. This means that there is a low microbial eukaryote diversity in the Movile Cave. Also, Alveolata and Stramenopiles seem to be the most abundant protist superphyla in the Movile Cave. Movile cave organization (Forti et al. 2002) Movile cave in the world (google maps) Microbial eukaryotes Metazoa Procaryotes HAS BEEN STUDIED HAS BEEN STUDIED OUR PROJECT Protist diversityMetabarcoding 18S analyses Phylogenomic analysesMetatranscriptomic analyses Sequencing and Bioinformatic approaches, methods, pipelines and analyses A high number of OTUs (197) are found in only one sample but a small number of reads (4,769). This means that there are minor divergent species in the Movile Cave depending on the environment (plankton, floating mats). Rhizaria (mostly Cercozoa) is the most abundant protist superphylum in the Movile Cave according to our results. Also, noted the presence of Apusozoa and Hacrobia superphyla not present in the multiple sample analyses (to investigate). Multiple parameter combinations were tested in order to choose the best couple of criteria for our analyses Sample 1 Sample 2 Sample 3 Suboxic biofilm • floating mat • fixed in EtOH Suboxic biofilm • live mat • 2 months at room T° in the dark Plankton • all water filtered • 0.2-200 µm fraction retained DNA Extraction, sample preparation for sequencing Legend : tag to remember from which sample comes from the read : tag to select or discard an OTU for the downstream analyses Movile cave floating mat Microbial eukaryotes (Protists)